The Development and Application of FET-based Biosensors*

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Ph.D. in Biotechnology

XXIX Course

Genotoxicology of Engineered Nanomaterials (ENMs): the experience and data from a comparison

of genome-wide analyses in yeast and in plant.


Prof. Nelson Marmiroli Tutors:

Prof. Marta Marmiroli Dr. Caterina Agrimonti Dr. Jason C. White

Candidate: Francesco Pasquali



Summary... 3

List of abbreviations... 6

1. Introduction... 9

1.1 Abstract... 11

1.2 Nanotechnologies... 12

1.3 Engineered nanomaterials and risk assessment...13

1.4 Nanotoxicology... 14

1.5 State of the art and current legislation... 15

1.6 Synthesis of engineered nanomaterials... 16

1.7 Nanomaterials classification... 17

1.8 Metal oxide nanoparticles ... 18

1.9 Quantum dots... 20

1.10 Relevance of mitochondrion in eukaryotic cells...22

1.11 Cucurbita pepo as a model organism for nanomaterial exposure in crop plants...24

1.12 Saccharomyces cerevisiae as a model organism for nanotoxicological studies...25

1.13 Aim of the project... 26

2. Materials and methods... 28

2.1 CdS quantum dot synthesis and characterization...30

2.2 Cucurbita pepo and conditions of growth... 30

2.3 Physiological analyses and metal content evaluation...31

2.4 Gene expression analysis of C. pepo in response to ENM/NMCC treatment...31

2.5 Yeast strains and growth conditions... 32

2.6 Yeast growth on different carbon sources... 32

2.7 Cytofluorimetric analysis of yeast response to CdS QDs...33

2.8 Microarray transcriptomic analysis of yeast exposed to CdS QDs...33


2.9 Analysis of oxygen consumption and assessment of respiratory cytochrome content...34

2.10 Analysis on the effect of mitochondrial DNA integrity...34

2.11 Effect of CdS QDs on mitochondrial morphology...36

2.12 Evaluation of reactive oxygen species and glutathione redox state...36

2.13 Statistical analysis... 37

3. Cucurbita pepo: results and discussion... 39

3.1 Previous studies – Cucurbita pepo... 41

3.2 CdS quantum dot synthesis and characterization...41

3.3 Physiological analysis of C. pepo upon ENMs exposure...43

3.4 Photosynthetic efficiency and cell viability assays...46

3.5 ICP-MS analysis of ENMs uptake and translocation...47

3.6 Transcriptional analysis of C. pepo in response to ENM/NMCC treatment...53

3.7 Comparison of the transcriptional response to CdS QDs in C. pepo and A. thaliana...59

3.8 Overall PCA of metal uptake, molecular response and biomass...60

4. Saccharomyces cerevisiae: results and discussion...61

4.1 Growth phenotype high-throughput analysis of yeast deletion mutants...62

4.2 Growth on different carbon sources... 62

4.3 Cytofluorimetric analysis of yeast response to CdS QDs...63

4.4 Microarray transcriptomic analysis of yeast in response to CdS QDs...64

4.5 Analysis of oxygen consumption and assessment of respiratory cytochrome content...69

4.6 Analysis on the effect of mitochondrial DNA integrity...71

4.7 Effect of CdS QDs on mitochondrial morphology...72

4.8 Evaluation of reactive oxygen species and glutathione redox state...74

5. Conclusions... 76

6. References... 78

7. Acknowledgements... 95


8. Appendix... 96

8.1 Genes analyzed in C. pepo... 96

8.2 Microarray data... 98

8.3 Publications and conference proceedings... 134


List of abbreviations

QSAR - Quantitative Structure-Activity Relationship QDs - Quantum Dots

ENMs - Engineered Nanomaterials NMCC - Nanomaterial Co-Contaminant SWCNTs - Single-Walled Carbon Nanotubes

ESEM - Environmental Scanning Electron Microscope S/TEM - Scanning/Transmission Electron Microscope

HRTEM - High Resolution Transmission Electron Microscope XRD - X-ray diffraction

EDX - Energy-Dispersive X-ray analysis FWHM - Full Width at Half Maximum YPD - Yeast extract Peptone Dextrose YPG - Yeast extract Peptone Glycerol TTC - 2,3,5-Triphenyltetrazolium Chloride DAPI - 4',6-Diamidino-2-Phenylindole GSH - Glutathione reduced form GSSG - Glutathione oxidized form ROS - Reactive Oxygen Species DCF - 2’-7’-Dichlorofluorescein



DCFDA - 2’,7’-Dichlorodihydrofluorescein Diacetate DTNB - 5,5’-Dithiobis-2-Nitrobenzoic Acid

TNB - 2-Nitro-5-Thiobenzoic Acid

RS - Respiratory Sufficient


RD - Respiratory Deficient

RT-qPCR - Real Time quantitative Polymerase Chain Reaction PCA - Principal Component Analysis

ANOVA - Analysis of Variance

ICP-MS - Inductively Coupled Plasma Mass Spectrometry HSD - Honestly Significant Difference

GO - Gene Ontology

mtRFP - mitochondrial directed Red Fluorescent Protein


1. Introduction


1.1 Abstract

Engineered nanomaterials (ENMs) are structures on the range of 1-100 nm, and are

characterized by properties due to their small size and surface reactivity that make them suitable for

several industrial applications. Nanotechnology is a rapidly growing industry, with a market value

expected to reach US$ 55 billion by 2022 (Allied market research, 2016). Because of their wide

diffusion and of the lack of information about mechanisms of biological uptake and interaction with

cells, it’s crucial to assess the risks linked to their spread and behaviour in the environment. A first

part of this work was performed in plants, following a previous experience with Arabidopsis

thaliana L. Heynh (Marmiroli et al. 2014), in which transcriptomic analysis on two Ds

transposition-induced mutant lines allowed the identification of genes involved in tolerance to

cadmium sulphide quantum dots (CdS QDs). Starting from here, in cooperation with CAES of New

Haven, CT, the work focused on the impact of nanomaterials on crop plant Cucurbita pepo L. Five

different nanomaterials (CeO


, La




, CuO, ZnO, and CdS QDs) were tested singularly and in

couples to determine how the interaction between different NPs could alter their effect on the

transcriptome and their uptake and translocation inside plant tissues. Real-time expression analysis

identified several genes that specifically respond to each of the nanomaterials tested; in particular

the gene 152u, encoding for a chloroplastic electron carrier, is down-regulated in all the treatment

conditions, thus representing a putative biomarker of exposure. ICP-MS analysis of metal content

showed that nanomaterials are more easily translocated in stems and leaves than their bulk

counterparts, probably because of their smaller size. The aim of the second part of this work was to

analyse the effect of CdS QDs in the model system Saccharomyces cerevisiae, exploiting high-

throughput genomic and transcriptomic approaches: the former consisted in screening a collection

of 6000 haploid strains, with a deletion in genes that are not essential for yeast’s survival; the latter

consisted in a whole-transcriptome analysis of expression levels through Affymetrix GeneChip

Microarray platform. A gene ontology and network analysis was performed on the data obtained,


allowing the identification of mitochondrial organization and mitosis as the main biological processes affected by CdS QD action. In particular, data collected highlighted the differences between CdS QDs and Cd


mechanisms of toxicity, while HSC82, ALD3 and DSK2 were identified as some of the key genes involved in CdS QDs response (Marmiroli et al. 2016). The impact of quantum dots on mitochondria was then studied from a physiological and morphological point of view: i) the respiratory activity was impaired; ii) mitochondrial membrane potential was disrupted iii) fluorescence microscopy highlighted an interruption in the mitochondrial network; iv) upon treatment, reactive oxygen species accumulation was induced, while the glutathione redox- state decreased significantly; v) CdS QD treatment impaired the ability of yeast cell to grow on non- fermentable carbon sources but, conversely from ionic cadmium, did not induce the formation of respiratory deficient mutants (Pasquali et al. 2016).

1.2 Nanotechnologies

According to the European Union recommendation 2011/696/EU, a nanomaterial can be defined as “a natural, incidental or manufactured material containing particles, in an unbound state or as an aggregate or as an agglomerate and where, for 50 % or more of the particles in the number size distribution, one or more external dimensions is in the size range 1 nm - 100 nm”.

Engineered nanomaterials (ENMs) are gaining more consideration in the last decades due to their

peculiar properties, which make them suitable for a wide range of applications: agricultural

production and crop protection (Dwivedi et al. 2016), food processing (Peters et al. 2016),

environmental remediation (Louie et al. 2016), medicine (Li et al. 2016), electronics (Mohiuddin et

al. 2016) and many more. For this reason, nanotechnology market share reached 14 billion $ in

2015, and is expected to reach 55 billion $ by 2022 (Allied market research, 2016). In terms of

market share the categories with the higher income include inorganic non-metallic ENMs (like


synthetic amorphous silica, aluminium oxide, titanium dioxide), carbon based ENMs (like carbon black, carbon nanotubes), metal nanoparticles and organic, macromolecular or polymeric materials.

1.3 Engineered nanomaterials and risk assessment

ENMs behaviour is strongly influenced by their small size, high surface/volume ratio, chemical stability and composition (Wani et al. 2016). Furthermore, because of their peculiar properties, their fate and mechanism of action are usually different from the ones of their bulk counterparts. Another key factor playing a role in nanomaterial toxicity is their occurrence as aggregates or free particles: in the first case, the formation of agglomerates might mitigate the nano- specific properties, while in the second case exposure is likely to be more dangerous. Exposure may occur at different levels (fig. 1): i) at the production stage, where the risk is higher but easily controlled by the use of proper personal protective equipment and close systems; ii) at the use stage, where exposure is strongly influenced by the inclusion of ENMs in a matrix or their enclosure inside the products; iii) at the waste stage, after disposal and grinding of ENMs containing products.

Furthermore, an additional way of exposure to ENMs derives from their presence in recycled

materials. Exposure to ENMs that are proved not to be toxic and not to bio-accumulate represent a

lesser risk, as they won’t cause toxic effects at least at moderate doses. The same applies to those

ENMs who are confined in a matrix or whose effects are prevented by applying risk management

measures. Scientific Committee on Emerging and Newly Identified Health Risks (SCENIHR) stated

that “while risk assessment methodologies for the evaluation of potential risks of substances and

conventional materials to man and the environment are widely used and are generally applicable to

nanomaterials, specific aspects related to nanomaterials still require further development. This will

remain so until there is sufficient scientific information available to characterise the harmful effects

of nanomaterials on humans and the environment.” Therefore, it is necessary to develop new tools


and procedures to assess all the risks and safety issues arising from the dispersion of this new class of materials in the environment.

Figure 1. Routes of exposure and dispersion of ENMs in the environment (Keller et al. 2013).

1.4 Nanotoxicology

In this framework of risk characterization and evaluation, nanotoxicology represents a

powerful tool for the study of the effect of ENMs on biological systems. In the recent years, ENMs

hazards have been evaluated using both in vitro, in vivo and in silico techniques. The in vitro tests

include the use of single cell types cultures, co-cultures, three dimensional models of tissues and

cell-free assays (Stone et al. 2016); the abundance of tests allows the analysis of different

parameters at the same time, composing an overall view of ENMs toxicity that needs the validation

o n in vivo models and the use of a set of standards to compare different datasets. In silico

approaches, in particular quantitative structure-activity relationship (QSAR) analysis, aim to link

physiochemical properties of the ENMs to their behaviour in the biological systems, to allow the


production of “safer-by-design” ENMs and reduce the risks for human health. This kind of tests might represent a good alternative to the in vivo animal models, but they are still in the early stages of development (Tantra et al. 2015).

Considering the widespread diffusion of ENMs, their physical and chemical diversity as well as the current unreliability of the in silico approaches, the identification of testing platforms alternative to the expensive and ethically questionable animal models becomes crucial. Several governmental organizations, like EU (through its REACH project), the OECD (Organisation for Economic Co-operation and Development) and the US National Research Council (Krewski et al.

2010), are engaged in the development of alternative high-throughput tests based on simpler organisms, like Saccharomyces cerevisiae as a model for higher eukaryotes whereas C. pepo for agri-food relevant crop plants. Guidance from the international regulatory agencies (EFSA and FDA respectively for EU and US) requires a suite of in vivo and in vitro assessments that must be carried out for nanomaterial containing products. The two approaches may provide different and, sometimes, contradictive results: in fact the dose in vitro might not be relevant in vivo or the in vitro cell line might not be representative for the whole organism; furthermore effects of nanoparticles on health depend on individual factors such as genetics and existing disease. For these reasons, despite the respective limitation, both in vivo and in vitro tests provide necessary information that are complementarily needed to assess the ENMs mechanism of action and toxicity.

1.5 State of the art and current legislation

Even if ENMs safety regulation, recommendations and guidances are being developed to

allow a safer use of ENMs, both in EU and in other countries there are still no pieces of legislation

that are specific for them (Arts et al. 2014). For this reason ENMs are usually included in more

generic categories of chemical and are subjected to pre-existing regulations. For instance, in Europe

ENMs used in the different industrial sectors must meet the requirements of the REACH


(Registration, Evaluation and Authorization of Chemicals) (Regulation (EC) No 1907/2006), which regulates the production and utilization of all the chemical substances and their effects on environment and health. In many cases, the current legislation is considered to be sufficient to allow a safe use of nanotechnologies, but due to their growing heterogeneity and diffusion, a more stringent regulation might be required (Lee R.G. et al. 2016). At the moment, the European Parliament, Food and Drug Administration and several non-governmental organisations are actively operating in this field, developing a risk governance framework to address issues like the definition of nanomaterial, registration and authorisation procedures, risk assessment, risk management, traceability and labelling (Falkner and Jaspers, 2012).

1.6 Synthesis of engineered nanomaterials

ENMs production can exploit either bottom-up or top-down approaches (fig. 2). In the bottom-up approach, small building blocks are assembled through chemical reactions, nucleation or self-aggregation to produce complex structures. Organic synthesis, self-assembling, colloidal aggregation and laser-induced assembling techniques belong to this category. Bottom-up approach is used for the synthesis of fullerenes and carbon nanotubes (Sathish et al. 2009, Hitosugi et al.

2011). In the top-down approach, the production starts from the bulk materials which are processed through photolithography, quenching or mechanical techniques to obtain the ENMs. Top-down approaches are primarily used for the production of metal oxide nanoparticles (Yadav et al. 2012).

The choice between the two approaches depends on the properties required for the final product: the

bottom-up methods allow to precisely tune the size and the composition of the ENM, but the high

complexity of these processes make them less suitable for large-scale production; on the other hand,

top-down techniques have been optimized for industrial production, but result in an higher

heterogeneity of the ENMs in terms of dimensions. Recently, some studies are trying to combine

the advantages of both the approaches: for example, Huang et al. (2016) describe a manufacturing


process for hybrid anodes for lithium-ion batteries, which include a top-down synthesis of nano- silicon followed by a bottom-up inclusion in a nitrogen-doped graphene nanosheet. This process results in the production of batteries with a longer cycling lifetime and a better capacitive retention.

Figure 2. Bottom-up (a) and top-down (b) approaches for ENMs manufacture (Silva 2006).

1.7 Nanomaterials classification

A first classification of nanomaterials can be performed according to their natural or manufactured source. The first category includes all the nanomaterials produced by geological (e.g.

chemical and physical degradation of rock materials, neoformation, volcanic eruptions) or

biological (e.g. nucleic acids, peptides, viruses) processes. Living organisms evolved in an

environment where these natural nanomaterials are actively released and interact with other

pollutants, water and organic matter (Handy et al. 2008). The engineered nanomaterials are

designed to meet specific properties and characteristics and are synthesized through technological

processes. Engineered nanomaterials can be furthermore subdivided by their composition in: i)


carbon-based ENMs, including fullerenes, nanotubes and graphene (fig. 3); ii) metal-based ENMs, including single metals (e.g. Au and Ag nanoparticles), metal-oxides (e.g. CeO


, La




, TiO


nanoparticles) and nanocrystals such as semi-conductor quantum dots; iii) hybrid ENMs, combining the two aforementioned categories through functionalization or the use of a core-shell structure to modulate ENMs reactivity and target specificity.

Figure 3. Different kinds of carbon based ENMs.

1.8 Metal oxide nanoparticles

Metal oxide nanoparticles are one of the most widely used ENMs for industrial applications.

The high variety of oxide compound used for their synthesis along with the ease of modification and the tunability of their physical and chemical properties, makes them suitable for use in construction materials (e.g. insulators, semiconductors, paint, Lee J. et al. 2010), cosmetics (e.g.

ZnO and TiO


used in sunscreen, Lu et al. 2015), agriculture (e.g. CuO and ZnO used as biocides,

Bondarenko et al. 2013) and in the biomedical field (e.g. antibacterial iron oxide nanoparticles,

Javanbakht et al. 2016). Their modifiable characteristics result in a wide range of effects, primarily

dependent on charge, shape, size and degree of dispersion. For instance, semi-conductor nano-

oxides seem to exert a higher toxic effect in bacteria, if compared to the non-insulators (Bohnsack

et al. 2012); more soluble metal oxide nanoparticles, like ZnO, are easier to disperse and to be


internalized in the cell, where they induce the accumulation of reactive oxygen species and the response of cell to oxidative stress (Xia et al. 2008). In human lung epithelial cells, ZnO and Al




nanoparticle exposure resulted in the activation of the NFκB pathway and induced the release of inflammatory cytokines; furthermore ZnO induced also the main mitogen-activated protein kinases (Simón-Vázquez et al. 2016). Patil et al. (2016) reported that exposure of lung fibroblasts to TiO


and ZnO nanoparticles resulted in a dose-dependent epigenetic alteration, as proved by the decrease in global DNA methylation and DNA methyltransferase activity. In A. thaliana, CuO nanoparticles significantly inhibited plant growth and induced root damage; after 2 hours of exposition, nano- CuO induced oxidative stress in roots to a greater extent if compared to the corresponding bulk material, as shown by reactive oxygen species accumulation and up-regulation of oxidative stress- related genes (Tang et al. 2016). CuO nanoparticles exposition induced a decrease in sees germination rates in both cucumber (Moon et al. 2014) and rice (Shaw and Hossain 2013). ZnO, Fe




, Al




, CuO, nanoparticles highlighted a negative impact on shoot/ root growth and elongation in many crop species such as rice, wheat, maize, tomato, and barley (Rizwan et al.

2017). Furthermore, metal oxide nanoparticles had been shown to induce chromosomal aberrations in maize (Castiglione et al. 2011) and garlic (Shaymurat et al. 2012).



nanoparticles are extensively used as semiconductors in solar cells, UV blockers,

polishing agents and their photocatalytic properties are exploited in chemical and mechanical

cleaning methods (Balavi et al. 2013). La




nanoparticles are used in biomedical sensors for

temperature, uric acid and glucose detection (Brabu et al. 2015). Thanks to their paramagnetic

properties, La




nanoparticles are being tested for targeted drug delivery in the human body

(Zhang X. et al. 2016). CuO nanoparticles have applications as catalysts in chemical reactions and,

thanks to their excellent electric properties, in the manufacture of superconductors, solar cells,

sensors and storage devices (Singh et al. 2016). They also find application in agriculture as

fungicides (Kanhed et al. 2013). ZnO nanoparticles have a potential outcome in agriculture too, as


nanofertilizers and nanopesticides (Sabir et al., Kah et al. 2014), and are used for photocatalysis and sunscreens (Faure et al. 2013).

1.9 Quantum dots

Quantum dots (QDs) are a class of extremely small in size semi-conductors ( Ø < 10 nm),

composed by metals belonging to groups II-VI or III-V of the periodic table. Conversely to

traditional dyes, QDs exhibit a narrow emission and a broad excitation spectrum, making them

suitable for bioimaging and biosensing approaches (Jamieson et al. 2007). Thanks to their tuneable

absorption spectrum, combined with a high extinction coefficient, QDs find an application in the

synthesis of aerogel components of photovoltaic panels (Xing et al. 2016). Due to their intrinsic

optic, magnetic, electric and piezoelectric properties, QDs are widely used in the manufacturing of

batteries, led screens and lasers (Zhai et al. 2010). Quantum dots’ mechanism of action and possible

toxicity are strongly affected by different factors, like composition, size, surface charge,

presence/absence of a shell (or other surface modifications) and interactions with proteins or other

bio-ligands (Oh et al. 2016, fig. 4).


Figure 4. Schematic representation of the structure of QDs, highlighting all the factors that influence their behaviour and the possible interactions with other ligands (Oh et al. 2016).

The cadmium sulfide quantum dots (CdS QDs) that have been used during this study were provided by IMEM-CNR (Istituto dei Materiali per l’Elettronica e il Magnetismo, Parma, Italy). They were synthesized through a wet-chemistry approach, according to Villani et al. 2012, Cadmium acetate 99.99% (Cd(CH




)), N,N-dimethylformamide 99% (HCON(CH




) and thiourea 99.5%





). These quantum dots have an average diameter of 4~5 nm, a density of 4.82 g cm


and an average weight of 2.5*10



In literature there are several evidences of quantum dots’ toxic impact on human health and

environment, which is correlated to their surface properties, functionalization, diameter, assay type

and exposure time (Oh et al. 2016). The study of Nguyen et al. (2015) analyzed the response of

human hepatocellular carcinoma HepG2 cells to CdTe quantum dots exposure: QDs associate to the

mitochondria, leading to a disruption of mitochondrial membrane potential and to an impairment of

cellular respiration. CdTe QDs caused also a change in the activity and the levels of the enzymes of


the electron transport chain. The comparison of the response to equivalent concentrations CdCl


showed how the toxic effects of QDs were not only dependent on release of free ionic cadmium.

Zhang T. et al. (2015) highlighted how in vivo and in vitro exposure of CdTe QDs in mice liver cells resulted in an increased level of lipid peroxide markers and in a concentration- and time- dependent cytotoxic effect. An accumulation of reactive oxygen species and the induction of apoptosis were also observed. In particular, the up-regulation of the tumor suppressor gene p53 and of the pro-apoptotic gene Bcl-2, and the down-regulation of the anti-apoptosis gene Bax, suggested a central role of the mitochondrion in the activation of programmed cell death pathways. The study of Fan et al. (2016) showed how CdTe/CdS core/shell quantum dots activate autophagy instead of apoptosis in HL-7702, HepG2, HEK-293 and Raji cell lines, as confirmed by confocal and TEM microscopy. Accumulation of reactive oxygen species was suggested as one of the causes of autophagy induction and cytotoxic effects. In vivo tests on BALB/c mice resulted in liver injury, nephrotoxicity, and hematopoietic disorders, whose symptoms were ameliorated after administration of an autophagy repressor.

1.10 Relevance of mitochondrion in eukaryotic cells

Mitochondrion is a double-membrane organelle, which plays a central role in eukaryotic

cells. They possess a circular genome, remnant of the endosymbiotic origin of these organelles. The

majority of the genes required for mitochondrial biogenesis were transferred into the nuclear

genome during the evolution of eukaryotes, and only a small amount is still encoded by

mitochondrial DNA (mtDNA). In yeast, this genome was sequenced for the first time in 1998

(Foury et al. 1998): it consists of a 75 kb circular molecule, encoding for 28 proteins and 27

different ribosomal and transfer RNA. Mitochondria are considered the “powerhouse” of the cell, as

they are involved in generation of ATP through the oxidative phosphorylation, controlling the basic

rates of cellular metabolism (McBride et al. 2006). Mitochondria are also responsible for


biosynthesis of iron-sulfur clusters, heme, lipid and sterols (Lasserre et al. 2015). Furthermore, apoptosis could be triggered by mitochondria through three different mechanisms: the first is dependent on the caspase family of proteases, which are triggered through the release of specific proteins from the mitochondria; the other two are caspase-independent and rely on interruption of oxidative phosphorylation and alteration of cellular redox potential (fig. 5, Jin C. et al. 2002). As a result of evolution, mitochondrial structure and functions have been modified across the different eukaryotes, thus conserving the essential role of this organelle. So far, Monocercomonoides sp. is the only eukaryote devoid of functional mitochondria: some of their functions, in particular iron- sulfur cluster assembly pathway, are provided by cytosolic machinery probably acquired through bacterial lateral gene transfer (Karnkowska et al. 2016). In humans, defects in mitochondrial functionality are related to a number of severe diseases, including hyperglycaemia-induced coronary microvascular dysfunction, hypertensive renal disease, obesity, diabetes, muscular disease, cancer and Parkinson’s disease (Nunnari et al. 2012, Van Houten et al. 2016).

In a previous study (Marmiroli et al. 2016), a screening of a deletion mutant collection of

Saccharomyces cerevisiae identified mitochondrial organization as one of the main biological

processes affected by CdS QD exposition. A transcriptional analysis performed on HepG2 human

cell line (Paesano et al. 2016) highlighted that the same CdS QDs trigger mitochondria-mediated

apoptotic pathway. Furthermore, in literature are reported other evidences that mitochondria is a

major target of nanomaterial's toxicity: Ye et al. 2016 show that positively charged graphene

induces mitochondrial fission and recruitment of dynamin-related protein in BV-2 cell line; TiO


nanoparticles affect lung mitochondrial function in animal cell lines (Freyre-Fonseca et al. 2011),

while in utero exposition of rat pups induces mitochondrial dysfunction (Stapleton et al. 2015).


Figure 5. Mechanisms of caspase-dependent and -independent apoptosis mediated by mitochondria in mammalian cells (Jin C. et al. 2002).

1.11 Cucurbita pepo as a model organism for nanomaterial exposure in crop plants

Cucurbita pepo L. is a plant belonging to the family of Cucurbitaceae. It is considered as

one of the most important vegetable crops from an economical and agricultural point of view,

thanks to its high content in vitamins, minerals and fibers (Obrero et al. 2011). C. pepo is a

monoicous species, with distinct male and female flowers on the same plant. Its genome size may

vary from 945.7 to 1084.6 Mb among the different zucchini cultivars (Rayburn et al. 2008). Due to

its wide use and distribution, in the last years C. pepo is gaining a growing interest as a model

organism for toxicology and nanotoxicology studies: Musante and White (2010) reported that Ag


and Cu nanoparticles treatments in C. pepo result in a decrease in biomass and transpiration;

Stampoulis et al. (2009) showed that Cu nanoparticles reduced emerging root length by 77% and 64% if compared to untreated control and bulk Cu treatment, while Ag nanoparticles and multiwalled carbon nanotubes negatively affected plant's biomass; Hawthorne et al. (2012) demonstrated that Au nanoparticles did not affect C. pepo growth at any particle size and concentration tested, whereas Si nanoparticle treatment resulted in a reduction of plant growth and transpiration; in addition, the solution properties were found to significantly impact the nanoparticle's effects. In the last years, in particular the focus is on transcriptional response (Pagano et al. 2016), uptake and translocation of pollutants in plant tissues and fruits (Tripathi et al. 2016), and their transfer along the food chain (Hawthorne et al. 2014). For this reason, several databases and bioinformatics tools are being developed: the transcriptome has been fully characterized ( B l a n c a e t a l. 2 0 1 1 ) , a n d t h e g e n o m e s e q u e n c e s a r e a v a i l a b l e o n l i n e (

1.12 Saccharomyces cerevisiae as a model organism for nanotoxicological studies

Saccharomyces cerevisiae is a unicellular eukaryote and one of the most used model organisms for molecular biology. Its genome has a size of ~12 Mb, subdivided in 16 chromosomes, which has been completely sequenced and annotated (Goffeau, 2000). Due to its ease of use, fast life cycle (one division each 90 minutes) and to the availability of several molecular and genomic tools, yeast had been used as a platform for toxicological studies (Dos Santos et al. 2015).

Furthermore the high level of functional conservation within higher eukaryotes genomes, in

particular human, makes yeast a key model system for the assessment of the mechanisms

underlying the response to environmental pollutants as ENMs. For instance, yeast cells exposed to

CuO nanoparticles did not affect cellular viability, but resulted in an inhibition of metabolic activity

after copper release in the growth media (Mashock et al. 2016). Otero-González et al. (2013)


studied the effects of different ENMs (TiO


, ZrO


, Fe


, Fe




, and Mn




) on yeast: manganese- based ENMs had a consistent impact on inhibition of oxygen consumption and membrane integrity, whereas the others exhibited a lower or absent toxicity. Bayat et al. (2014) analysed the cytotoxic and morphological effects of TiO2, CuO, ZnO, Ag nanoparticles and single-walled carbon nanotubes (SWCNTs) on S. cerevisiae. CuO nanoparticles exerted a high cytotoxic effect, and reduced cell density by 80%; Ag and TiO


nanoparticles reduced cell density and localized at intracellular vacuoles, cell wall and vesicles levels. ZnO NPs were non cytotoxic and resulted in an increase of the size of the vacuoles. SWCNTs, despite the potential induction of oxidative stress evidenced in other studies, were not cytotoxic and did not induce alteration in the cell structure. As reported in other several studies (e.g. Bermejo-Nogales et al., Yang et al., Khalid et al. 2016), oxidative stress induction by nanomaterial exposure affects mitochondrial functionality (disruption of mitochondrial structures, permeabilization of inner membrane). The capability to grow in mitochondrial impairment conditions makes S. cerevisiae a perfect model organism for the study of this “pathological mitochondria” condition.

1.13 Aim of the project

The aim of these work was to evaluate ENMs impact on C. pepo and S. cerevisiae from a

molecular and physiological point of view: the first revealed the genes and biological processes

most affected by ENMs exposure, while the latter focused the attention on the effects on growth and

homeostasis of the two model organisms in condition of treatment; in particular, toxicity of

nanomaterials was assessed in conditions of acute exposition (high concentrations and short times

of exposure): these conditions are not likely to occur in the environment, but give a better

understanding of ENMs mechanisms of action and toxicity and allow the identification of putative

biomarkers of exposure to nanomaterials, as a part of a broader framework of risk assessment.


In the first part, the response of C. pepo to binary combinations of ENMs was investigated.

In literature several papers analyse the response of zucchini to single types of ENMs, but currently no one has investigated how NPs-NPs interactions influence their uptake, translocation and effect on gene transcription. In particular, impact on physiological parameters (roots/shoots length, water content, photosynthetic pigments abundance), on nanoparticles uptake and translocation and on gene transcription were assessed.

The aim of the second part of the project was the in-depth investigation of the mechanism of

action of CdS QDs, using S. cerevisiae as a model organism for higher eukaryotes. High throughput

assays (deletion mutant screening and microarray transcriptome analysis) provided a general

overview of the genes and biological processes involved in CdS QDs response. According to

previous studies and evidences in literature, the role of mitochondria as one of the main targets of

quantum dots toxicity has been analysed, using a molecular biology approach, physiological tests

and fluorescence microscopy.


2. Materials and methods


2.1 CdS quantum dot synthesis and characterization

Cadmium sulphide quantum dots used were provided by IMEM-CNR (Parma, Italy) and synthesized from cadmium acetate, N,N-dimethyl formamide and thiourea through a wet-chemistry approach, according to Villani et al. 2012. Different analyses were carried out to determine CdS QDs properties: i) X-ray diffraction was analysed with an ARL-X’Tra device (Thermo Fisher Scientific, Waltham, MA, USA); their structure was examined with a field emission high resolution JEM-2200 FS transmission electron microscope (JEOL ltd., Tokyo, Japan) operating at 200 kV; iii) group morphology and elemental content was assessed with an environmental scanning electron microscope (ESEM) Quanta 250 FEG, FEI with Bruker QUANTAX EDS XFlash® 6T detector series and ESPRIT 2 analytical methods interface (FEI company, Hillsboro, Oregon, USA; Bruker, Berlin, Germany). For electron microscopy, single drops of an 80 mg L


solution of CdS Quantum dots were deposed on SEM stub covered with carbon tape and let dry. Scanning electron imaging and X-ray spectra acquisition were performed at a pressure of 70 Pa, a working distance of 9.9 mm, and an acceleration voltage of 20 KeV.

2.2 Cucurbita pepo and conditions of growth

Cerium oxide (CeO


, nanopowder, <25 nm particle size) was purchased from Sigma Aldrich; Lanthanum oxide (La




, 10-100 nm particle size range), copper oxide (CuO, 40 nm particle size), and zinc oxide (ZnO, <100 nm particle size) were purchased from US Research Nanomaterials, Inc. CdS QDs were provided by IMEM-CNR. Every nanomaterial was characterized through z-sizer and scanning/transmission electron microscopy (S/TEM), according to Pagano et al. 2016. All the respective bulk materials were purchased from Sigma Aldrich.

Zucchini seeds (Cucurbita pepo L., cv Costata Romanesco) were purchased from Johnny’s Selected

Seeds (Albion, ME, USA). Seeds were sown in vermiculite for seven days. After the germination,

seedlings were transferred in 100 mL of vermiculite containing 0 mg L


(untreated control), 500 mg




ZnO (NPs or bulk), 100 mg L


CdS (QDs or bulk), or binary combinations of the five ENMs.

Prior to use, nano- and bulk materials solutions were sonicated through a Fisher Scientific model 505 sonic dismembrator (Fisher Scientific, Waltham, MA, USA) at 40% amplitude for 60-120 s.

Seedlings were treated for 21 days, following Pagano et al. 2016.

2.3 Physiological analyses and metal content evaluation

After the exposure, plants were harvested and thoroughly washed in deionized water. For each plant, fresh weight and primary roots and shoots length were measured. For metal content analysis each plant was divided in roots, stem and leaves in five biological replicates. Samples were dried and digested in 65% HNO


, and then analyzed with an Agilent ICP-MS CE 7500 (Agilent Technologies, Santa Clara, CA, USA), according to Pagano et al. 2016. 0.2 g of leaves for each plant were harvested and prepared for 2,3,5-triphenyltetrazolium chloride (TTC) and chlorophyll/carotenoids content analysis, following Marmiroli et al. 2014.

2.4 Gene expression analysis of C. pepo in response to ENM/NMCC treatment

Total RNA was extracted, in three biological replicates per treatment, from 0.1 g of fresh

plant material, using a Sigma Aldrich Spectrum Plant Total RNA Kit (Sigma Aldrich). 0.1 ng of

total RNA were retro-transcribed to cDNA with a RT-qPCR Qiagen QuantiTect Reverse

Transcription kit (Qiagen, Velno, NL). Analysis of gene expression was performed on a subset of

38 genes, using actin β-actin of C. pepo as housekeeping gene, according to Pagano et al. 2016. The

list of the 38 genes is reported in paragraph 8.1 of the appendix section.


2.5 Yeast strains and growth conditions

The majority of the experiments discussed in this thesis used Saccharomyces cerevisiae strain BY4742 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0). For petite induction studies, W303 (MATα leu2-3,112 trp1-1 can1-100 ura3-1 ade2-1 his3-11,15) strain was used in addition to BY4742. Yeast cells were inoculated in a liquid YPD (Yeast extract Peptone Dextrose) medium pre-culture (1%

w/v yeast extract, 2% w/v peptone, 2% w/v glucose), and grown for 24 h at 28°C under shaking at 130 rpm. Pre-culture cell concentration was assessed through Burker’s chamber count. 10


cells mL



were then inoculated in fresh YPD for negative control, treated in YPD supplemented with nystatin (0.25 mg L


), nystatin + CdS quantum dots (CdS QDs, 75, 100 or 150 mg L


), cadmium sulphate (CdSO


, 10, 20 or 25 µM) or ethidium bromide (50 µg mL


), as specified for each single experiment. Nystatin is a polienic antibiotic, which binds the ergosterol of yeast cell’s membrane used to encourage the uptake of CdS QDs, as described in Marmiroli et al. 2016. Cultures were then grown at 28°C under shaking at 130 rpm.

2.6 Yeast growth on different carbon sources

Four serial 10-fold dilutions (10




) of either wild type and RD cells were spotted onto YPD alone, supplemented with 0.55 mg L


nystatin or 0.55 mg L


nystatin plus 100/150/200 mg L



CdS QDs. The concentrations of nystatin and CdS QDs were increased, in respect of liquid YPD treatments, due to their lower bioavailability in the solid medium, according to Marmiroli et al.

2016. RD mutants were used as negative control of growth on non-fermentable carbon sources. The

experiment was replicated on YP agar plates where 2% w/v dextrose was replaced with other

fermentable (galactose) or non-fermentable (glycerol and ethanol) carbon sources. After 72 h of

incubation at 28 °C, the presence/absence of growth at each cell dilution was assessed.


2.7 Cytofluorimetric analysis of yeast response to CdS QDs

For cytofluorimetric analysis, BY4742 cells were grown in absence of treatment, or treated with 0.25 mg L


of nystatin or nystatin plus 100 mg L


CdS QDs. 10


cells per each treatment were then harvested by centrifugation (5000 g, 5 min) and stained with FUN1 (CAS number: 161057-69- 8), a stain that is internalized in the vacuole by living cells only. Its accumulation leads to the formation of typical red-orange fluorescent structures (Wenish et al. 1997). For each treatment, both stained and unstained samples were analysed with an Attune NxT Flow Cytometer (Thermo Fisher Scientific, Waltham, MA, USA) using a BL1 filter.

2.8 Microarray transcriptomic analysis of yeast exposed to CdS QDs

Microarray analysis was carried out on BY4742 cells, treated for 24 h on liquid YPD without supplementation, with 0.25 mg L


nystatin, or with 0.25 mg L


nystatin plus 100 mg L


CdS QDs. Five biological replicates were performed for each treatment. Total RNA was extracted from the samples with an RNeasy Mini kit (Qiagen, Velno, NL): Extraction yield and quality was assessed through spectrophotometric analysis and electrophoresis in a 2% w/v agarose gel. For each sample, two aliquots of RNA were retro-transcribed into cDNA using a QuantiTect Reverse Transcription Kit (Qiagen, Velno, NL). Transcriptome analysis was performed through Affymetrix GeneChip Yeast genome 2.0 array platform (Affymetrix, Santa Clara, CA, USA), covering 99.93%

of S. cerevisiae’s genome. RT-qPCR analysis of expression was performed on a subset of genes to

validate microarray experiment’s results, according to Marmiroli et. al 2014, using PDA1 as

housekeeping gene.


2.9 Analysis of oxygen consumption and assessment of respiratory cytochrome content

BY4742 cells were treated for 24 hours in YPD liquid medium, in presence of all the CdS QD and CdSO


concentrations. Around 10


cells were harvested through centrifugation (5000 g, 5 min) and re-suspended in 1.5 mL of double-distilled water. The peak height of cytochrome aa3, b and c was then used to calculate the cytochrome content through the formula here reported:

Cytochrome content = (peak height*range)/(sample’s dry weight).

Oxygen consumption was measured on 100 µL of the cell suspension, which were transferred into an Oxygraph reaction chamber (Hansatech Instruments, King's Lynn, UK) with 800 µL of air- saturated 0.1 M phthalate–KOH (pH 5.0) and 1.5 µM of glucose. Another 1 mL aliquot was analysed in parallel to acquire cytochrome spectra, using a Varian Cary 219 UV-VIS dual-beam spectrophotometer (Agilent Technologies, Santa Clara, CA, USA). Both assays were conducted in three biological replicates.

2.10 Analysis on the effect of mitochondrial DNA integrity

BY4742 and W303 cells were treated for 24 h in liquid YPD, in presence of nystatin, CdS QDs (all the concentrations), CdSO


(20 µM) and ethidium bromide. 100 cells per each treatment were then collected by centrifugation (5000 g, 5 min), washed and re-suspended in double-distilled water, and plated on YPD supplemented with agar (2% w/v). Cells were grown for 3-5 days at 28

°C. For BY4742 cells, mitochondrial function was assessed following Ogur et al. 1957. This assay takes advantage of the ability of respiratory sufficient (RS) cells to reduce TTC into red-pigmented 1,3,5-triphenylformazan (fig. 6). On the other hand, respiratory deficient (RD) cells are unable to reduce TTC, so their colonies remain non-pigmented. Each treatment was conducted in triplicate.

To confirm the RD/RS phenotype, colonies were sub-cloned onto YPG agar (1% w/v yeast extract,

2% w/v peptone, 2% w/v glycerol), as RS (red) cells are able to grow on YPG, while RD (white)


cannot. For W303 strain, cells were cultured for 24 h on liquid YPD under the same treatment conditions, after which ~100 cells were plated onto YPD agar. For the analysis of mtDNA integrity, DNA was extracted following Harju et. al 2004 from 5x10


BY4742 cells exposed to CdS QDs after 24 h of growth. The relative abundance of mitochondrial markers was analysed through quantitative Real Time PCR (RT-qPCR). The DNA extracted from each sample was amplified for 40 cycles of 95°C/15 s, 60°C/60 s, followed by a dissociation curve step to confirm the single amplicons. Specific primers were designed with the software package Primer 3 v 0.4.0 ( for housekeeping gene PDA1 and for mitochondrial genes COX2, ATP6 and COB. Each sample was analysed in duplicate using an ABIPRISM 7000 Real-Time PCR System (Life Technologies Carlsbad, CA, USA). ΔΔCt method was used to determine the relative abundance in the different conditions of treatment, compared to the control (untreated).

Figure 6. In the TTC assay, 2,3,5-triphenyltetrazolium chloride is reduced to 1,3,5-

triphenylformazan by mitochondrial dehydrogenases.


2.11 Effect of CdS QDs on mitochondrial morphology

To assess mitochondrial integrity, wt BY4742 and RD cells were treated with nystatin alone and 100 mg L


CdS QDs. 3x10


cells for each treatment were then harvested through centrifugation (5000 g, 5 min), and stained with two different dyes: DAPI (Sigma Aldrich), which binds A-T rich regions in DNA (including the mitochondrial genome, see Baruffini et al. 2010), and rhodamine B hexyl ester perchlorate (Life Technologies), which selectively binds the mitochondrial membrane (Bornhövd et al. 2006). Each sample was analysed with an Axio Imager 2 microscope (Carl Zeiss, Oberkochen, Germany) equipped with a DAPI filter (excitation and emission wavelengths: 359 nm and 461 nm) and a rhodamine filter (excitation and emission wavelengths: 528 and 551 nm).

Mitochondrial network integrity was determined through transformation of BY4742 cells (Gietz and Woods, 2002) with the pYX142-mtRFP plasmid, provided by the laboratory of Prof. J.

Winderickx. This plasmid expresses a mitochondrial-localized red fluorescent protein (Van Rossom et al. 2012). 10


cells mL


of the transformed strain were treated for 24 h in selective SC-LEU medium, in presence of either 100 mg L


CdS QDs or 20 µM CdSO


. Samples were analysed with an Axio Imager 2 microscope (Carl Zeiss) equipped with an RFP filter (emission wavelength 588 nm). About 500 cells per treatment were analysed to determine the effect of treatment on the abundance of the different mitochondrial morphotypes.

2.12 Evaluation of reactive oxygen species and glutathione redox state

Accumulation of Reactive Oxygen Species (ROS) and of glutathione oxidized/reduced

forms (GSSG/GSH) was assessed by measuring the formation of 2',7'-dichlorofluorescein (fig. 7 a,

Bussche and Soares, 2011) and 2-nitro-5-thiobenzoate (fig. 7 b, Flattery-O’Brien and Dawes, 1998),

respectively. Both assays were performed on about 10


cells harvested from each treatment (control,

nystatin and all the CdS QDs concentrations). For ROS analysis, a cell lysate was prepared at zero,

one and four hours after the beginning of the treatment. Fluorescence was measured with a


SpectraFluor Plus fluorimeter (Tecan Group Ltd., Männedorf, Switzerland, excitation and emission wavelengths: 485 nm and 535 nm). For oxidized (GSSG) and reduced (GSH) glutathione quantifications, an iMARK microplate absorbance reader (Bio-Rad, Hercules, CA, USA, emission wavelength: 415 nm) was used. The glutathione redox state was calculated from the ratio GSH/


Figure 7. A) Cleavage of diacetate group from 2’,7’-dichlorodihydrofluorescein diacetate



DCFDA) and oxidation by reactive oxygen species (ROS) to fluorescent 2’-7’- dichlorofluorescin (DCF), source:; b) reduced glutathione (GSH) oxidation by 5,5’- dithiobis-2-nitrobenzoic acid (DTNB) to give oxidized glutathione (GSSG), with formation of fluorescent 2-nitro-5-thiobenzoic acid (TNB), as described in Araujo et al. 2008.

2.13 Statistical analysis

The microarray raw data were analysed using Expression Console v1.4.0 software ( following protocols provided by Biolitix AG (Witterswill, SUI).

Gene references were cited from Saccharomyces Genome Database (


Interaction networks were built using the GeneMANIA data service (

Cytofluorimetric data were analysed with Attune NxT software. Statistical analysis, principal

component analysis, heatmaps and cluster analysis were performed with R statistical software

( For each experiment, the normality of the distribution was assessed with the

Shapiro-Wilk’s test; variance homogeneity was determined through ANOVA; comparisons between

treated and untreated samples were performed with Tukey’s pairwise comparison (in the relative

graphs, statistically different means are indicated with different letters).


3. Cucurbita pepo: results and discussion


3.1 Previous studies – Cucurbita pepo

A previous study (Pagano et al. 2016) analyzed the physiological and transcriptional response of C. pepo in response to 500 mg L




, La




and CuO NPs and to their bulk and salt counterparts. In particular, CeO


NPs did not dramatically impact zucchini physiology. Conversely, La




and CuO NPs significantly decreased plant moisture content, roots/shoots length and total biomass. Treatment with bulk materials had only minor effects on zucchini plants, which highlights the particle-size specific nature of the plant response. ICP-MS metal content analysis on different plant tissues highlighted that zucchini effectively translocated La from roots to both stems and leaves, while Ce does not accumulate in leaf tissues. Not surprisingly, Cu is actively translocated in leaves too, but S/TEM analysis did identify nano-Cu aggregates. Although, bulk materials treatments also resulted in an accumulation of Ce, Cu and La in roots, stems and leaves, in general the content was lower than in the corresponding ENM. This difference was most likely due to the different particle size and element form. Transcriptional analysis focused on 38 C. pepo orthologs o f A. thaliana genes that were up- or down-regulated in response to CdS QDs (Marmiroli et al.

2014). The analysis identified 7 genes whose expression is consistently modulated in response to CeO


, La




and CuO NPs treatment either. Of these, only BIP3, which encodes for a heat shock protein 70 (Hsp70) with ATP-binding function, was commonly up-regulated even in A. thaliana in response to CdS QDs, and therefore might represent a general biomarker of susceptibility.

3.2 CdS quantum dot synthesis and characterization

Figure 8 a shows QDs aggregation, observed upon solvent evaporation due to the lack of

capping molecules at QDs surface. The corresponding reduced Fourier transform (FT) in the inset

confirms the hexagonal structure (greenockite, P63mc) of as-synthesized CdS QDs (d=0.36 nm in

agreement with JCPDS no. 80-0006). The FT of the whole HRTEM image is presented in figure 8


b: The expected ring feature coming from the random orientation of the CdS crystallites is observed, as is the overlap of (100), (002) and (101) reflections of the wurtzite structure (at high d values) due to low-dimension peak broadening. Such features are in agreement with XRD pattern reported in figure 8 c. All peaks have been indexed according to greenockite structure and no other reflections arising from possible impurities are observed. Scherrer calculation, based on FWHM of the three main peaks of the reported pattern, results in an average size of about 6 nm. Figures 9 a and b show SEM image of CdS QDs drop, at 29750x and 130802x magnification respectively. In these pictures, the nanocrystals are grouped into small aggregates with a diameter of 50-100 nm.

EDX spectra (fig. 9 c) was performed on the point indicated in figure 9 b: Lα1 and Lβ1 emission lines are visible for cadmium at 3.133 and 3.316 eV, while Kα1 and Kβ1 lines at 2.308 and 2.464 eV are visible for sulphur.

Figure 8. HRTEM image of ligand-free QDs assembly: (a) a CdS QDs aggregate upon solvent

evaporation; (b) Fourier transform analysis of the whole HRTEM image. (c) X-ray diffraction



Figure 9. ESEM/EDX image of ligand-free CdS QDs assembly at 80 mg L


concentration. (a) and (b) panels show SEM images of CdS QDs at different magnifications (29750 x and 130802 x respectively). The green arrow in panel (b) indicates the point where energy-dispersive X-ray analysis (EDX) was performed. (c) EDX spectra of CdS QDs, reporting X-ray emission lines for Cd and S.

3.3 Physiological analysis of C. pepo upon ENMs exposure

Bulk ZnO and CdS QDs significantly affected the plant biomass. (-47.4% and -21.9%

respectively, tab. 1). In general, bulk- and ENMs alone did not affect the water content in leaves,

stems or roots (tab. 2); the only exception is represented by the leaves of plants treated with bulk

ZnO, which showed an increase of 82.96% of the water content. Treatment with ZnO NPs resulted

in an increase of 37% of the root length (tab. 3), contrary to the findings of Zhang R. et al. 2015 that

highlighted a reduction of 17% in other Cucurbitaceae. However, these discrepancies might be due

to the different concentrations tested (500 mg L


vs 1000 mg L


), to the different growing

conditions or to species-specific effects. The other treatments did not show any significant

differences in the physiological parameters analyzed. The different behavior of bulk and

corresponding nano-counterparts could be in part explained considering the different amounts of Cd

and Zn that were found in the different plant tissues (see paragraph 3.5 ICP-MS analysis of ENMs

uptake and translocation).


Table 1. Biomass (g) of C. pepo treated with bulk or nano ZnO and CdS

sample biomass stdev t-test Control

(untreated) 4.054 0.610 -

ZnO NPs 4.640 0.537 0.14607

ZnO Bulk 2.134 0.459 0.00064***

CdS QDs 2.762 0.280 0.00589**

CdS Bulk 3.340 0.576 0.09354

Table 2. Water content (g/g) of C. pepo treated with bulk or nano ZnO and CdS

sample leaves st dev t-test stem

s st dev t-test roots st dev t-test Control

(untreated) 19.239 7.008 - 0.972 0.021 - 44.333 24.066 -

ZnO NPs 19.079 1.253 0.96229 0.970 0.006 0.8151

8 43.133 23.879 0.93886

ZnO Bulk 35.200 12.84

9 0.04936* 0.986 0.003 0.2158

4 34.600 10.526 0.44195

CdS QDs 25.448 5.555 0.16111 0.977 0.011 0.6799

8 31.200 9.358 0.30524

CdS Bulk 23.712 3.775 0.25461 0.977 0.005 0.6241

4 49.400 11.739 0.68745

Table 3. Length (cm) of stems, roots and shoots of C. pepo treated with bulk or nano ZnO and CdS

sample stems st dev t-test roots st dev t-test Control

(untreated) 18.340 24.00

0 - 3.669 6.255 -

ZnO NPs 18.580


0 0.92347 3.979 2.530 0.02698*

ZnO Bulk 14.560


0 0.09399 2.317 6.940 0.89306

CdS QDs 16.200


0 0.26803 0.967 4.115 0.68439

CdS Bulk 18.620


0 0.89097 2.435 5.962 0.07229

Among the binary combination treatments with each of the different ENMs, only La




NPs + ZnO

NPs induced a significant change (+27.4%) in the total biomass of the plant (tab. 4). CdS QDs and



NPs had respectively the greatest and smallest impact on plant biomass production when

combined with other ENMs. Root water content was increased by each combination with La





while conjunction with CuO lead also to an increase of stem water content (+1.64%) (tab. 5). Root

length was not significantly affected by treatment, while stem length was increased by CeO



combination with CuO or ZnO (+17% and +13% respectively), by La




in combination with ZnO


and CdS (+27.27% and +21.17% respectively) and by treatment with CuO and ZnO (+21.67%) (tab. 6).

No significant differences were highlighted in photosynthetic efficiency or cell viability, as shown by chlorophyll A, B, carotenoids and TTC content data (fig. 8). Ceriums effect on shoot length aligns with findings from previous work (Pagano et al. 2016, Ma et al. 2010), while the reduction of shoot length by La




(Pagano et al. 2016) seems to be prevented by the concomitant treatment with other NPs. As reported in Ma et al. 2010, La




NPs alone induced ROS production and programmed cell death, leading to a decrease in root/shoot length and total biomass. In general, each combination containing ZnO NPs caused the increase in roots water content (2.54–3.6 %).

Table 4. Biomass (g) of C. pepo treated with binary combination of the different ENMs. *, **

and *** indicate a p value respectively less than 0.05, 0.01 and 0.001.

sample biomass st dev t-test Control (untreated) 2.858 0.671 - CeO2 NPs + La2O3 NPs 3.542 0.821 0.18870 CeO2 NPs + CuO NPs 4.174 1.065 0.05338 CeO2 NPs + ZnO NPs 3.566 0.724 0.14771 CeO2 NPs + CdS QDs 2.188 0.564 0.12705 La2O3 NPs + CuO NPs 2.96 0.275 0.76521 La2O3 NPs + ZnO NPs 3.938 0.774 0.04682*

La2O3 NPs + CdS QDs 3.624 1.082 0.22251 CuO NPs + ZnO NPs 3.412 0.935 0.31611 CuO NPs + CdS QDs 2.54 0.824 0.52297 ZnO NPs + CdS QDs 3.096 0.379 0.51450


Table 5. Water content (g/g) of C. pepo treated with binary combination of the different ENMs. *, ** and *** indicate a p value respectively less than 0.05, 0.01 and 0.001.

sample leaves st dev t-test stem

s st

dev t-test roots st

dev t-test

Control (untreated) 22.300 5.975 - 0.971 0.003 - 0.944 0.017 -

CeO2 NPs + La2O3 NPs 19.790 11.33

8 0.67658 0.963 0.005 0.01323* 0.953 0.013 0.38967 CeO2 NPs + CuO NPs 12.577 1.668 0.01958* 0.950 0.010


* 0.946 0.008 0.81577

CeO2 NPs + ZnO NPs 26.195 6.320 0.34600 0.977 0.009 0.21812 0.968 0.014 0.04122*

CeO2 NPs + CdS QDs 24.558 12.39

2 0.72667 0.982 0.008 0.03423* 0.970 0.004 0.02668*

La2O3 NPs + CuO NPs 41.633 23.28

8 0.13820 0.987 0.009 0.01532* 0.981 0.003 0.00868**

La2O3 NPs + ZnO NPs 20.988 5.749 0.73263 0.972 0.004 0.65138 0.973 0.007 0.01612*

La2O3 NPs + CdS QDs 26.678 11.10

0 0.46631 0.978 0.006 0.06570 0.974 0.010 0.01419*

CuO NPs + ZnO NPs 29.327 7.102 0.13004 0.978 0.010 0.21173 0.977 0.009 0.00884**

CuO NPs + CdS QDs 19.973 6.575 0.57442 0.978 0.004 0.01909* 0.954 0.011 0.33531 ZnO NPs + CdS QDs 38.150


6 0.12161 0.976 0.006 0.12472 0.978 0.006 0.01009*

Table 6. Length (cm) of C. pepo treated with binary combination of the different ENMs. *, **

and *** indicate a p value respectively less than 0.05, 0.01 and 0.001.

sample shoots st dev t-test roots st dev t-test

Control (untreated) 16.06 1.22 - 29.72 7.65 -

CeO2 NPs + La2O3 NPs 18.98 2.07 0.03219* 29.74 5.70 0.99638 CeO2 NPs + CuO NPs 18.8 1.68 0.02043* 31.8 3.27 0.59840 CeO2 NPs + ZnO NPs 18.16 1.18 0.02472* 25.62 4.47 0.33793 CeO2 NPs + CdS QDs 14.84 0.97 0.12082 24.66 5.79 0.27446 La2O3 NPs + CuO NPs 17.86 2.83 0.24376 26.54 2.84 0.42272 La2O3 NPs + ZnO NPs 20.44 1.97 0.00431*

* 30.12 5.63 0.92748

La2O3 NPs + CdS QDs 19.46 2.28 0.02536* 25.92 2.36 0.33930 CuO NPs + ZnO NPs 19.54 1.26


* 28.12 8.55 0.76320

CuO NPs + CdS QDs 17.24 1.37 0.18971 24.3 4.52 0.21795 ZnO NPs + CdS QDs 16.66 1.46 0.50109 23.5 2.76 0.14754

3.4 Photosynthetic efficiency and cell viability assays

As reported in figure 10, colorimetric assessment of chlorophyll A, B and carotenoids did

not show a significant change in comparison with the untreated control, meaning that exposure to

the combinations of ENMs tested had no measurable impacts on photosynthetic efficiency. Also the

1,3,5-triphenylformazan content, which provides a measure of cell viability, was mostly unaffected

by treatment except for the case of CuO NPs + ZnO NPs combination, which showed a significant


increase (+90.2%). It’s important to note that the lack of statistical differences in the content of photosynthetic pigments and TTC might be due to the standard deviation in some of the analysed samples.

Figure 10. Chlorophyll A and B, carotenoids and 1,3,5-triphenylformazan contents in C. pepo

plants treated with binary combinations of the five ENMs tested. (*) indicates a p value less than 0.05.

3.5 ICP-MS analysis of ENMs uptake and translocation

ICP-MS analysis performed on C. pepo tissues grown in absence of treatment or in presence

of either bulk or nano ZnO (fig. 11) showed that: i) Zn is actively translocated in leaves; its content

is higher in condition of treatment with ZnO NPs rather than with correspondent bulk material,

probably because of the different size; ii) Zn content is significantly lower in NPs treatment,




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